mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
d67eba0f31
* add: utility for image grid. * add: return type. * change necessary places. * add to utility page.
72 lines
2.7 KiB
Markdown
72 lines
2.7 KiB
Markdown
# Textual inversion
|
|
|
|
[[open-in-colab]]
|
|
|
|
The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).
|
|
|
|
This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](./training/text_inversion) training guide.
|
|
|
|
Login to your Hugging Face account:
|
|
|
|
```py
|
|
from huggingface_hub import notebook_login
|
|
|
|
notebook_login()
|
|
```
|
|
|
|
Import the necessary libraries:
|
|
|
|
```py
|
|
import os
|
|
import torch
|
|
|
|
import PIL
|
|
from PIL import Image
|
|
|
|
from diffusers import StableDiffusionPipeline
|
|
from diffusers.utils import make_image_grid
|
|
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
|
```
|
|
|
|
Pick a Stable Diffusion checkpoint and a pre-learned concept from the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer):
|
|
|
|
```py
|
|
pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
|
|
repo_id_embeds = "sd-concepts-library/cat-toy"
|
|
```
|
|
|
|
Now you can load a pipeline, and pass the pre-learned concept to it:
|
|
|
|
```py
|
|
pipeline = StableDiffusionPipeline.from_pretrained(
|
|
pretrained_model_name_or_path, torch_dtype=torch.float16, use_safetensors=True
|
|
).to("cuda")
|
|
|
|
pipeline.load_textual_inversion(repo_id_embeds)
|
|
```
|
|
|
|
Create a prompt with the pre-learned concept by using the special placeholder token `<cat-toy>`, and choose the number of samples and rows of images you'd like to generate:
|
|
|
|
```py
|
|
prompt = "a grafitti in a favela wall with a <cat-toy> on it"
|
|
|
|
num_samples = 2
|
|
num_rows = 2
|
|
```
|
|
|
|
Then run the pipeline (feel free to adjust the parameters like `num_inference_steps` and `guidance_scale` to see how they affect image quality), save the generated images and visualize them with the helper function you created at the beginning:
|
|
|
|
```py
|
|
all_images = []
|
|
for _ in range(num_rows):
|
|
images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images
|
|
all_images.extend(images)
|
|
|
|
grid = make_image_grid(all_images, num_samples, num_rows)
|
|
grid
|
|
```
|
|
|
|
<div class="flex justify-center">
|
|
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/textual_inversion_inference.png">
|
|
</div>
|